OpenAI Agents SDK - AgentX

Install the integration extra:

pip install "agentx-python[openai-agents]" openai-agents

Usage

Register AgentXTracingProcessor once at startup. It hooks into the OpenAI Agents SDK’s global tracing pipeline, so every subsequent Runner.run() call is traced automatically with no per-call changes.

from agentx import AgentX
from agentx.integrations.openai_agents import AgentXTracingProcessor
from agents import Agent, Runner, add_trace_processor, function_tool

client = AgentX.from_env()  # AGENTX_API_KEY (+ AGENTX_API_BASE_URL for self-host)

add_trace_processor(AgentXTracingProcessor(
    tracer=client.tracer,
    metadata={"env": "production"},
    session_id="session-001",
))

@function_tool
def get_policy(topic: str) -> str:
    """Return the company policy for a given topic."""
    db = {
        "cancel": "Go to Account → Subscription → Cancel.",
        "trial": "14-day free trial, no credit card required.",
        "refund": "Full refund within 30 days.",
    }
    return db.get(topic.lower(), "No policy found.")

agent = Agent(
    name="support-agent",
    instructions="You are a helpful support agent. Use get_policy to look up policies.",
    tools=[get_policy],
)

result = Runner.run_sync(agent, "How do I cancel my subscription?")
print(result.final_output)

client.tracer.flush(timeout=10)

What gets traced

Each top-level agent run (one Runner.run() / Runner.run_sync() call) produces one trace.

Field	Source
`input`	Extracted from the root span’s `input`
`output`	`trace.output` at run end
`latencyMs`	Wall-clock time from `on_trace_start` to `on_trace_end`
`model`	Extracted from the first LLM generation span
`toolCalls`	Each function-tool span: `tool_name`, `input`, `output`, `latencyMs`
`error`	`span.error` from the first failed span in the trace (message + data)

`AgentXTracingProcessor` reference

AgentXTracingProcessor(
    tracer: Tracer,
    metadata: dict | None = None,
    session_id: str | None = None,
)

Parameter	Description
`tracer`	`client.tracer` from your `AgentX` instance
`metadata`	Static key-value metadata attached to every trace
`session_id`	Links traces from the same conversation thread

The processor also implements force_flush() and shutdown(), which both call tracer.flush(). These are invoked automatically by the OpenAI Agents SDK at process exit.

Call tracer.flush() before your process exits in scripts or one-shot jobs. In long-running servers it is not required: traces drain automatically in the background.